Tim Whiteaker, The University of Texas at Austin (United States)Patricia Ganey-Curry, The University of Texas at Austin (United States)

A case study is presented in which a geological database for the Gulf of Mexico is restructured to adopt the schema of the Arc Hydro Groundwater data model. The term Arc Hydro refers to a Geographic Information Systems (GIS) data model for representing surface water features, which not only preserves cartographic depictions of those features, but also supports integration of the GIS with hydrologic and hydraulic simulation models. As Arc Hydro has become a standard used by several federal, state, and local surface water databases in the Untied States, a Groundwater extension of Arc Hydro has evolved for representing multidimensional groundwater data. A groundwater toolkit is concurrently in development, which enables visualization of hydrogeologic data in 2D and 3D environments. Thus, the Groundwater data model not only promises a convenient structure for storing hydrogeologic data, but also promotes data interoperability through the use of a data standard, and provides a sophisticated suite of tools ready to operate on the data. As a test of the feasibility in adopting the Groundwater data model, the GIS database for the Gulf of Mexico Basin Depositional Synthesis project (GBDS) is migrated to the Groundwater data model. GBDS is a highly successful joint industry-sponsored research program, which provides a complete and comprehensive picture of basin-scale sedimentation as a tool for reservoir prediction in the Gulf of Mexico. GBDS stores geologic data in its own GIS data model, which has been evolved and proven through the thirteen years of the project's history. Therefore, the challenge presented here is mapping components of the GBDS data model to features in the Groundwater data model, without losing important intricacies of the GBDS project, or sacrificing the most attractive qualities of the Groundwater data model. In this presentation, the Groundwater and GBDS data models are both introduced. Next, migration methodologies, issues and solutions are discussed, followed by an assessment of the benefits gains or functionality lost as a result of the migration. Throughout the migration process, strengths and weaknesses of both data models are highlighted.